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dc.contributor.authorClarelli, Fabrizio
dc.contributor.authorPalmer, Adam
dc.contributor.authorSingh, Bhupender
dc.contributor.authorStorflor, Merete
dc.contributor.authorLauksund, Silje
dc.contributor.authorCohen, Ted
dc.contributor.authorAbel, Sören
dc.contributor.authorAbel zur Wiesch, Pia
dc.date.accessioned2021-01-22T08:20:21Z
dc.date.available2021-01-22T08:20:21Z
dc.date.issued2020-08-14
dc.description.abstractAntibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92–94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.en_US
dc.identifier.citationClarelli F, Palmer A, Singh B, Storflor M, Lauksund R S, Cohen T, Abel S, Abel zur Wiesch P. Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. PLoS Computational Biology. 2020en_US
dc.identifier.cristinIDFRIDAID 1836148
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1008106
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.urihttps://hdl.handle.net/10037/20372
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.journalPLoS Computational Biology
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/FRIMEDBIO/262686/Norway/Predicting optimal antibiotic treatment regimens//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Medical disciplines: 700::Basic medical, dental and veterinary science disciplines: 710::Pharmacology: 728en_US
dc.subjectVDP::Medisinske Fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710::Farmakologi: 728en_US
dc.titleDrug-target binding quantitatively predicts optimal antibiotic dose levels in quinolonesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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